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Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
In this work, we propose a downscaling method based on the Fourier neural operator.
Aboveground carbon biomass estimate with Physics-informed deep network
Furthermore, models that learn from SIF-based GPP in addition to radar and optical imagery reduce validation RMSE by almost 10% and the standard deviation by 40%.
Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property.
A modular framework for extreme weather generation
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.
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